For massive translation requirements like the ones that the Google neural machine translation (GNMT) system is handling, it is necessary to distribute the complete workload into multiple processors and compute nodes.

The system is also optimized by extending the bucketing logic for combining similar length sentences into several nodes to attain load

GPU Cards

Proposed next steps:

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Our initial research provided several available insights and details on neural machine translation systems. Given that there are available resources on this, we propose continuing the research to provide additional insights and data points on the following topics:
(1) Provide 5-7 high-level insights that describe a computing node as it relates to Neural Machine Translation processes. The process should revolve around the tactic of adding a computing node to a server to increase throughput.
(2) Provide additional data on how much a computing node typically costs. Provide also the cost of a GPU.
(3) Provide 5-7 insights that explain how the Graphic Processing Unit (GPU) works, in relation to NMT if possible. We will include an explanation of how GPU inclusion affects translation speed as adding a GPU to a server is regarded as a common acceleration tactic.

We also recommend proceeding with additional research to provide 2-3 key players in the neural machine translation space. The major ones can be defined by their revenue sizes. For each player, we will provide the name, solutions being offered, why it is a key player, and the value proposition.